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Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

Author

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  • Alexander N. Kozlov

    (Energy Systems Institute of Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, Russia)

  • Nikita V. Tomin

    (Energy Systems Institute of Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, Russia)

  • Denis N. Sidorov

    (Energy Systems Institute of Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, Russia
    Baikal School of BRICS, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Electo E. S. Lora

    (Mechanical Engineering Institute, Federal University of Itajuba, Itajuba 37500-103, Brazil)

  • Victor G. Kurbatsky

    (Energy Systems Institute of Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, Russia)

Abstract

The importance of efficient utilization of biomass as renewable energy in terms of global warming and resource shortages are well known and documented. Biomass gasification is a promising power technology especially for decentralized energy systems. Decisive progress has been made in the gasification technologies development during the last decade. This paper deals with the control and optimization problems for an isolated microgrid combining the renewable energy sources (solar energy and biomass gasification) with a diesel power plant. The control problem of an isolated microgrid is formulated as a Markov decision process and we studied how reinforcement learning can be employed to address this problem to minimize the total system cost. The most economic microgrid configuration was found, and it uses biomass gasification units with an internal combustion engine operating both in single-fuel mode (producer gas) and in dual-fuel mode (diesel fuel and producer gas).

Suggested Citation

  • Alexander N. Kozlov & Nikita V. Tomin & Denis N. Sidorov & Electo E. S. Lora & Victor G. Kurbatsky, 2020. "Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques," Energies, MDPI, vol. 13(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2632-:d:361431
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    Cited by:

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    2. Tomin, Nikita & Shakirov, Vladislav & Kozlov, Aleksander & Sidorov, Denis & Kurbatsky, Victor & Rehtanz, Christian & Lora, Electo E.S., 2022. "Design and optimal energy management of community microgrids with flexible renewable energy sources," Renewable Energy, Elsevier, vol. 183(C), pages 903-921.
    3. Kafetzis, A. & Ziogou, C. & Panopoulos, K.D. & Papadopoulou, S. & Seferlis, P. & Voutetakis, S., 2020. "Energy management strategies based on hybrid automata for islanded microgrids with renewable sources, batteries and hydrogen," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    4. Lu, Jun & Liu, Tianqi & He, Chuan & Nan, Lu & Hu, Xiaotong, 2021. "Robust day-ahead coordinated scheduling of multi-energy systems with integrated heat-electricity demand response and high penetration of renewable energy," Renewable Energy, Elsevier, vol. 178(C), pages 466-482.
    5. Daniel de Cerqueira Lima e Penalva Santos & Juliana Berendt Ihler & Laene Oliveira Soares & Yana Amaral Alves & Cristina Gomez Souza & Ronney Arismel Mancebo Boloy, 2022. "Global Trends and Opportunities in Hybrid Microgrid Systems Using Renewable Energies," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 263-273, July.
    6. Nadia Cerone & Francesco Zimbardi, 2021. "Effects of Oxygen and Steam Equivalence Ratios on Updraft Gasification of Biomass," Energies, MDPI, vol. 14(9), pages 1-18, May.
    7. Lee, Jechan & Kim, Soosan & You, Siming & Park, Young-Kwon, 2023. "Bioenergy generation from thermochemical conversion of lignocellulosic biomass-based integrated renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).

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